Blue Feed/Red Feed: Filter Bubbles and Echo Chambers Examined

On May 18, 2016, Jon Keegan published a presentation on The Wall Street Journal called “Blue Feed/ Red Feed”. Two feeds are displayed on one page, side-by-side and are updated hourly. The blue feed on the left presents ‘very liberal’ posts from sources published on Facebook, whereas ‘very conservative’ is displayed on the right, covering topics such as “President Trump”, “Health Care”, “Guns”, “Abortion”, “Immigration”, “ISIS”, “Executive order” and “Budget”. The project is based on a study called “Exposure to ideologically diverse news and opinion on Facebook” that was previously conducted by the scientists Bakshy, Eyta and Messing, Solomon in 2015 in which they categorized numerous posts and analyzed the exposure of users to ideologically determined Facebook news.

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The researchers tracked and analyzed the top 500 shared sources as well as the content of 10.1 million Facebook user’s feeds, who themselves indicated their political views on their profile. From there, a political ‘alignment score’ is calculated for each article, in order to determine its political nature as well as the larger category that it will be put in, ranging from ‘very liberal’ to ‘very conservative’.

Hence, the data used for this project are these numerous private Facebook profiles as well as the findings of the 2015 Facebook research paper. In short, the project was conducted in order to expose how reality, in this case, a user’s Facebook feed, might appear drastically different for distinct users depending on their political views, due to potential filter bubbles or echo chambers. Consequently, this can result in users not keeping an open mind, which is essential for qualitative political debates.

The researchers used the software development tool Graph API, which is the primary tool to pragmatically extract information from Facebook. This also ensures that the content relevant to the research is being pulled from Facebook automatically. However, the software developer tools and the ‘alignment score’ employed by the researchers in order to determine which posts finally appear on the two feeds are not explained in detail and need to be further researched by the reader.

Despite the positive sides of this project, one can detect several possible limitations as well as potential improvements that could make it even more beneficial. For instance, the researchers set the terms that sources must have at least 100.000 followers and the included posts must have been shared at least 100 times by Facebook users, which excludes a lot of sources that still might have a potential influence on user’s political opinion. In addition, the fact that they used Facebook users’ self-described political orientation as a base for the source’s political value might lead to distortions and impreciseness. Furthermore, the project excludes the sources and websites that have been shared by users from in a broader political spectrum, including The Wall Street Journal, but also social media platforms like Twitter and Youtube, which might lead to a rather extreme simulation of a Facebook feed. Finally, one could argue that the five categories from ‘very liberal’ to ‘very conservative’ lead to a limited and oversimplified classification, especially considering that in the final project these are further narrowed down to two feeds. A possible proposition for the project would be to include Facebook users’ opinions in the form of surveys or opinion polls in order to examine whether they are aware that they scroll within a filter bubble or not.

 

To conclude, the project demonstrates that by indicating personal political views on the Facebook profile, it will influence what kind of posts and sources will appear on one’s News Feed. Therefore, the delivery of the project resulted in a successful visualization of both feeds side-by-side. Nevertheless, there are other aspects of the project and the research that are questionable and/or could have been done differently. For instance, the technique of collecting and categorizing data appeared to be limiting. Namely, certain posts and sources, who still could have a strong influence on one’s political opinion, were excluded from the research, because they did not fit within the established data collection framework. Additionally, to collect the data from Facebook, the software development tool Graph API was used; however, the execution and application of the tool were not thoroughly explained to the reader. Consequently, it was challenging to learn about the process of the project’s realization. Nevertheless, the project still succeeded at bringing attention and informing Facebook users of the threat of echo chambers and filter bubbles.

References

Bakshy,Eytan; Messing, Solomon, and Adamic, Lada A. 2015. “Exposure to ideologically diverse news and opinion on Facebook”. Science. https://education.biu.ac.il/files/education/shared/science-2015-bakshy-1130-2.pdf

Keegan, Jon. 2016. “Blue Feed, Red Feed”. WSJ. http://graphics.wsj.com/blue-feed-red-feed/#methodology.

 

 

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